MLOps with Databricks. Machine Learning and GenAI Applications End to End Maria Vechtomova

MLOps with Databricks. Machine Learning and GenAI Applications End to End Maria Vechtomova - okladka książki

Autor:: Maria Vechtomova
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 386
Dostępne formaty:: ePub

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MLOps engineers have to deal with a glut of tools and SaaS applications, not to mention technical debt clogging the system. Such complexity requires a comprehensive approach. The Databricks platform provides all the critical components for end-to-end MLOps and LLMOps in one place. This exhaustive book shows you how to use Databricks to build and manage a robust ML system that delivers on your business's needs.

Maria Vechtomova guides you through MLOps principles and explains how Databricks handles the machine learning lifecycle holistically, from data preparation to model deployment and monitoring, and enables data engineers, data scientists, and MLOps engineers to collaborate seamlessly. To put all the pieces together, you'll navigate two ML projects: a real-time ML application and an LLM-based system that highlights LLM-specific Databricks features.

Understand the Databricks components for MLOps and LLMOps
Unpack ML Model Serving architectures
Track your machine learning experiments and register your models
Build an ML application that uses Feature and Model Serving, and Model Serving with automatic feature lookup
Deploy a real-time ML application and an LLM-based application
Monitor your AI applications on Databricks
Understand how MLOps principles fit into AI governance

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Szczegóły książki

ISBN Ebooka:: 979-83-416-0822-1, 9798341608221
Data wydania ebooka :: 2026-08-07 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami [email protected].
Język publikacji:: angielski
Rozmiar pliku ePub:: 13.4MB
Rozmiar pliku Mobi:: 13.4MB

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Spis treści książki

Foreword by Matei Zaharia
Foreword by Alejandro Saucedo
Preface
- Why Databricks?
- Who and What This Book Is For
- How This Book Is Organized
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. MLOps Principles and Components
- The Rise of MLOps
- MLOps Principles
  - Traceability
  - Reproducibility
  - Reliability
  - Observability
- Team Practices
  - Documentation and Knowledge Preservation
  - Code Hygiene
- MLOps Components
  - Core Components
    - Code version control
    - Continuous integration and continuous deployment (CI/CD)
    - Workflow orchestration
    - Model and container registry
    - Model training
    - Model Serving
    - Monitoring
    - Data version control
    - Feature store
  - LLM Application Components
    - Vector databases
    - Guardrails
    - AI gateways
    - Memory stores
    - Observability tools
    - Evaluation tools
    - Human review and feedback
  - Choosing Your Tools
- Databricks MLOps Components
  - Lakeflow Jobs
  - Databricks Compute
    - Classic compute versus serverless compute
    - All-purpose versus job compute
  - MLflow
  - Feature Engineering
  - Databricks Model Serving
  - Data Quality Monitoring
  - Databricks AI Search
  - Unity AI Gateway
  - Databricks AI Guardrails
  - Cross-Cutting Components
    - Unity Catalog
    - Declarative Automation Bundles
    - Lakebase
    - Databricks Apps
- Conclusion
2. Developing on Databricks
- Cluster Configurations on Databricks
  - All-Purpose and Jobs Compute Configuration
    - Policies and access modes
    - Runtime versions
    - Photon acceleration
    - Worker and driver nodes
    - Advanced settings
    - Cost tracking
  - Serverless Compute
    - Serverless environments
    - Cost tracking
- Project Setup
  - The Dataset
  - Python Tooling
  - The pyproject.toml file
    - Project section
    - Build-system section
    - Linting and formatting
    - Pytest configurations
  - Project Code: Data Preprocessing
- Databricks Developer Tools
  - Databricks CLI
  - Declarative Automation Bundles
    - Syntax of the databricks.yml file
    - The databricks.yml file
    - Using DABs
  - Databricks Connect
  - Databricks Extension for Visual Studio Code
  - Running Code in a Databricks Notebook
    - Using serverless compute
    - Using all-purpose compute
- Conclusion
3. MLflow for Traditional ML
- Experiment Tracking
  - Tracking URI
  - MLflow Experiment
  - MLflow Run
- Training Data
- Hyperparameter Tuning and Nested Runs
- Logging and Registering Models with MLflow
  - Model Logging with the sklearn Flavor
  - Viewing the Logged Model
  - Registering the Model
  - Wrapping the Model Using pyfunc
- MLflow Evaluation
- Resource and API Limits
- Conclusion
4. Model Serving: Architectures and Implementation
- Model Serving
  - Endpoint Deployment
  - A/B Testing
    - Choosing a routing strategy
      - Randomization by customer
      - Session-level assignment
      - Request-level assignment
    - Moving assignment into the model logic
  - Debugging
- Feature Serving
  - Creating a Feature Table
  - Creating a FeatureSpec
  - Publishing a Table to a Databricks Online Feature Store
  - Endpoint Deployment
- Model Serving with Feature Lookup
  - Creating Feature Functions and a Feature Table
  - Defining the Training Set
  - Registering the Model and Publishing an Online Table
  - Payload Structure
- OAuth Authentication
- Serving Limitations
- Conclusion
5. Machine Learning Model Deployment
- Machine Learning Pipeline
  - Data Preprocessing
  - Training, Evaluating, and Registering a Model
  - Deploying a Model Serving Endpoint
  - Serverless versus Classic Compute for Lakeflow Jobs
- Databricks Platform Design
  - How Many Environments Do You Need?
  - Platform Best Practices
  - Access Control Strategy
- Promoting Code and Models to Higher Environments
  - Git Branching Model
  - Continuous Integration Pipeline
  - Continuous Deployment Pipeline
    - OpenID Connect
    - Branch and deployment protection rules
- Promoting Models to Higher Environments
- Testing
  - ML Model Testing
  - Software Testing
    - Unit testing
    - Databricks considerations
    - Integration testing
    - Practical considerations: Integration tests in CI
    - End-to-end testing
- Conclusion
6. Monitoring ML Applications
- What to Monitor
  - Recommender Systems
  - Regression and Demand Forecasting
  - Large Language Models
  - Concept Drift versus Data Drift
  - Fairness, Bias, and Regulatory Compliance
  - System and Infrastructure Health
- Introduction to Databricks Data Quality Monitoring
  - Profile Metrics Table
  - Drift Metrics Table
- Metrics for Monitoring Data Drift
  - Chi-squared Test
  - Kolmogorov-Smirnov Test
  - Total Variation Distance
  - L-Infinity Distance
  - Jensen-Shannon Distance
  - Wasserstein Distance
  - Population Stability Index
- From Inference Tables to Data Quality Monitoring
  - Inference Table Structure
  - Processing an Inference Table
  - Creating a Data Quality Monitor
  - Using the Dashboard
  - Refreshing the Monitor
- Alerting
- Conclusion
7. Foundation Models and Context Engineering
- Introduction to LLMOps
  - What Is an Agent?
  - Structure of the LLMOps Chapters
- Foundation Models on Databricks
  - Hosting Options
    - Databricks-hosted models (pay-per-token)
    - Provisioned throughput endpoints
    - External models
  - Types of Foundation Models
- Context Engineering
  - Reference Architecture: The arXiv Curator
  - Getting from Raw Data to Chunks
    - Project configuration
    - Data ingestion
    - PDF processing
    - Chunking
      - Chunking strategies
      - Embedding window considerations
      - Processing chunks
  - Databricks AI Search Index
    - Creating the endpoint and index
    - Querying and retrieval quality
    - Optimizing search quality
  - Genie Agent (Formerly, Genie Space)
    - Databricks AI Search versus Genie
    - Provisioning the SQL warehouse
    - Creating the Genie agent
    - Interacting with Genie
  - MCP Servers on Databricks and Tool Calling
    - Managed MCP ecosystems on Databricks
    - Implementing tool calling
  - Agent Memory with Lakebase
    - Lakebase Autoscaling for agent workloads
    - Lakebase Autoscaling project structure
    - Creating a Lakebase Autoscaling project
    - Interacting with Lakebase
    - Utilizing a connection pool
- Conclusion
8. MLflow for GenAI
- MLflow Flavors
- MLflow Tracing
  - Anatomy of a Trace
  - MLflow Tracing APIs
- Defining a Custom Agent
  - Initialization and Async Handling
  - Core Agent Logic
  - Prediction and Streaming
  - Testing the Agent
- LLM System Evaluation
  - How to Evaluate LLM Systems
  - MLflow GenAI Evaluation Framework
- Evaluating, Logging, and Registering the Agent
  - Evaluation in Practice
  - Agent Logging
  - Registering the Agent and Preparing for Serving
- Conclusion
9. Deploying in Monitoring LLM-Based Systems
- Deploying a Databricks Model Serving Endpoint
  - Unity AI Gateway and Guardrails
    - Guardrails on the LLM level
    - Guardrails as part of the agent logic
    - Implementing deployment
    - Setting up service principal authentication
    - Authentication considerations
    - Calling the Model Serving endpoint
  - Deployment Jobs
    - Data preprocessing job
    - Agent deployment job
  - Deployment Considerations
- LLM System Monitoring
  - What to Monitor in LLM Systems
  - Monitoring Implementation
    - Deployment notebook
    - Lakeflow job
    - Monitoring dashboard
    - Monitoring considerations
  - Cost Monitoring
    - How Databricks billing works
    - Databricks services and associated costs
    - Tag-based cost attribution
    - Cost-monitoring dashboard
- Conclusion
10. AI Governance
- The Scope of AI Governance
  - Definition of AI Under the EU AI Act
    - The challenge of definition: A three-factor approach
  - Risk Categories and Whom They Apply To
- Requirements for High-Risk AI
  - AI Project Lifecycle
  - Article 9: Risk Management System
    - Project scoping (risk identification)
    - Development and testing (mitigation)
    - Post-market monitoring (via a feedback loop)
    - Types of risks and mitigation strategies
      - Data-related and model behavior risks
      - Operational risks
      - Security and cybersecurity risks
  - Article 10: Data and Data Governance
    - Project scoping
    - Dataset/context engineering
    - Model development/adaptation
    - Deployment
    - Monitoring and continual learning
    - Business analysis
  - Article 11: Technical Documentation
  - Article 12: Record-Keeping
  - Article 13: Transparency and Provision of Information to Deployers
  - Article 14: Human Oversight
  - Article 15: Accuracy, Robustness, and Cybersecurity
- MLOps Checklist
- Conclusion
Epilogue
Index